Detection of anomalies in a time series using values of a different time series

ABSTRACT

In some implementations, sequences of time series values determined from machine data are obtained. Each sequence corresponds to a respective time series. A plurality of predictive models is generated for a first time series from the sequences of time series values. Each predictive model is to generate predicted values associated with the first time series using values of a second time series. For each of the plurality of predictive models, an error is determined between the corresponding predicted values and values associated with the first time series. A predictive model is selected for anomaly detection based on the determined error of the predictive model. Transmission is caused of an indication of an anomaly detected using the selected predictive model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.15/420,737 filed Jan. 31, 2017 and titled “ANOMALY DETECTION BASED ONRELATIONSHIPS BETWEEN MULTIPLE TIME SERIES,” the entire contents ofwhich are incorporated by reference herein.

BACKGROUND

Modern data centers often include thousands of hosts that operatecollectively to service requests from even larger numbers of remoteclients. During operation, components of these data centers can producesignificant volumes of raw, machine-generated data. In many cases, it isdesirable detected anomalies from such collected data.

SUMMARY

Embodiments of the present invention are directed to anomaly detectionbased on relationships between multiple time series. This summary is notintended to identify key features or essential features of the claimedsubject matter, nor is it intended to be used as an aid in determiningthe scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIG. 7 illustrates a user interface screen for an example datamodel-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example query received from a client and executedby search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIG. 12 illustrates illustrate an example visualization of time seriesdata sets in accordance with disclosed embodiments;

FIG. 13 illustrates an example of a data processing environment inaccordance with disclosed embodiments;

FIG. 14A illustrates an example of generating and selecting predictivemodels for a time series, in accordance with disclosed embodiments;

FIG. 14B illustrates a diagram used to describe training and evaluatinga predictive model in accordance with disclosed embodiments;

FIG. 15. illustrates a display including examples of indications ofanomalies in accordance with disclosed embodiments;

FIG. 16 is a flow diagram depicting an illustrative method in accordancewith disclosed embodiments;

FIG. 17 is a flow diagram depicting an illustrative method in accordancewith disclosed embodiments;

FIG. 18 is a flow diagram depicting an illustrative method in accordancewith disclosed embodiments; and

FIG. 19 is a block diagram of an example computing device in whichembodiments of the present disclosure may be employed.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

1.0. General Overview

2.0. Operating Environment

2.1. Host Devices

2.2. Client Devices

2.3. Client Device Applications

2.4. Data Server System

2.5. Data Ingestion

-   -   2.5.1. Input    -   2.5.2. Parsing    -   2.5.3. Indexing

2.6. Query Processing

2.7. Field Extraction

2.8. Example Search Screen

2.9. Data Modeling

2.10. Acceleration Techniques

-   -   2.10.1. Aggregation Technique    -   2.10.2. Keyword Index    -   2.10.3. High Performance Analytics Store    -   2.10.4. Accelerating Report Generation

2.11. Security Features

2.12. Data Center Monitoring

2.13. Cloud-Based System Overview

2.14. Searching Externally Archived Data

-   -   2.14.1. ERP Process Features

2.15. Searching Externally Archived Data

3.0. Anomaly Detection Based on Relationships Between Multiple TimeSeries

3.1. Anomaly Detection Tool in a Data Processing Environment

3.2. Generation and Selection of Predictive Models

-   -   3.2.1 Features of Predictive Models    -   3.2.1 Evaluation of Predictive Models    -   3.2.3 Approximation Mining

3.3. Anomaly Detection using Predictive Models

3.4. Illustrative Examples

3.5. Illustrative Hardware System

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104.

Networks 104 broadly represent one or more LANs, WANs, cellular networks(e.g., LTE, HSPA, 3G, and other cellular technologies), and/or networksusing any of wired, wireless, terrestrial microwave, or satellite links,and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a queryduring a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an example process that asearch head and one or more indexers may perform during a query. Atblock 402, a search head receives a query from a client. At block 404,the search head analyzes the query to determine what portion(s) of thequery can be delegated to indexers and what portions of the query can beexecuted locally by the search head. At block 406, the search headdistributes the determined portions of the query to the appropriateindexers. In an embodiment, a search head cluster may take the place ofan independent search head where each search head in the search headcluster coordinates with peer search heads in the search head cluster toschedule jobs, replicate search results, update configurations, fulfillsearch requests, etc. In an embodiment, the search head (or each searchhead) communicates with a master node (also known as a cluster master,not shown in Fig.) that provides the search head with a list of indexersto which the search head can distribute the determined portions of thequery. The master node maintains a list of active indexers and can alsodesignate which indexers may have responsibility for responding toqueries over certain sets of events. A search head may communicate withthe master node before the search head distributes queries to indexersto discover the addresses of active indexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other pipelined an non-pipelined query languages (e.g., theStructured Query Language (“SQL”)) can be used to create a query.

In response to receiving the query, search head 210 uses extractionrules to extract values for the fields associated with a field or fieldsin the event data being searched. The search head 210 obtains extractionrules that specify how to extract a value for certain fields from anevent. Extraction rules can comprise regex rules that specify how toextract values for the relevant fields. In addition to specifying how toextract field values, the extraction rules may also include instructionsfor deriving a field value by performing a function on a characterstring or value retrieved by the extraction rule. For example, atransformation rule may truncate a character string, or convert thecharacter string into a different data format. In some cases, the queryitself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “3.5ed fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a query 802 receivedfrom a client at a search head 210 can split into two phases, including:(1) subtasks 804 (e.g., data retrieval or simple filtering) that may beperformed in parallel by indexers 206 for execution, and (2) a searchresults aggregation operation 806 to be executed by the search head whenthe results are ultimately collected from the indexers.

During operation, upon receiving query 802, a search head 210 determinesthat a portion of the operations involved with the query may beperformed locally by the search head. The search head modifies query 802by substituting “stats” (create aggregate statistics over results setsreceived from the indexers at the search head) with “prestats” (createstatistics by the indexer from local results set) to produce query 804,and then distributes query 804 to distributed indexers, which are alsoreferred to as “search peers.” Note that queries may generally specifysearch criteria or operations to be performed on events that meet thesearch criteria. Queries may also specify field names, as well as searchcriteria for the values in the fields or operations to be performed onthe values in the fields. Moreover, the search head may distribute thefull query to the search peers as illustrated in FIG. 4, or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the query to the search peers. In this example, the indexersare responsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 806 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developer's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.15. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all query references toa Hadoop file system may be processed by the same ERP process, if theERP process is suitably configured. Likewise, all query references to anSQL database may be processed by the same ERP process. In addition, thesearch head may provide a common ERP process for common external datasource types (e.g., a common vendor may utilize a common ERP process,even if the vendor includes different data storage system types, such asHadoop and SQL). Common indexing schemes also may be handled by commonERP processes, such as flat text files or Weblog files.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the query. Upon determining that it has resultsfrom the reporting mode available to return to the search head, the ERPmay halt processing in the mixed mode at that time (or some later time)by stopping the return of data in streaming mode to the search head andswitching to reporting mode only. The ERP at this point starts sendinginterim results in reporting mode to the search head, which in turn maythen present this processed data responsive to the search request to theclient or search requester. Typically the search head switches fromusing results from the ERP's streaming mode of operation to results fromthe ERP's reporting mode of operation when the higher bandwidth resultsfrom the reporting mode outstrip the amount of data processed by thesearch head in the ]streaming mode of ERP operation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe query request, and calculating statistics on the results. The usercan request particular types of data, such as if the query itselfinvolves types of events, or the search request may ask for statisticson data, such as on events that meet the search request. In either case,the search head understands the query language used in the receivedquery request, which may be a proprietary language. One example of aquery language is Splunk Processing Language (SPL) developed by theassignee of the application, Splunk Inc. The search head typicallyunderstands how to use that language to obtain data from the indexers,which store data in a format used by the SPLUNK® Enterprise system.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a query request format thatwill be accepted by the corresponding external data system. The externaldata system typically stores data in a different format from that of thesearch support system's native index format, and it utilizes a differentquery language (e.g., SQL or MapReduce, rather than SPL or the like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the query). An advantage of mixed mode operation isthat, in addition to streaming mode, the ERP process is also executingconcurrently in reporting mode. Thus, the ERP process (rather than thesearch head) is processing query results (e.g., performing eventbreaking, timestamping, filtering, possibly calculating statistics ifrequired to be responsive to the query request, etc.). It should beapparent to those skilled in the art that additional time is needed forthe ERP process to perform the processing in such a configuration.Therefore, the streaming mode will allow the search head to startreturning interim results to the user at the client device before theERP process can complete sufficient processing to start returning anysearch results. The switchover between streaming and reporting modehappens when the ERP process determines that the switchover isappropriate, such as when the ERP process determines it can beginreturning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the query requestbefore the ERP process starts returning results; rather, the reportingmode usually performs processing of chunks of events and returns theprocessing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.14. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a query thatderives a KPI value from the machine data of events associated with theentities that provide the service. Information in the entity definitionsmay be used to identify the appropriate events at the time a KPI isdefined or whenever a KPI value is being determined. The KPI valuesderived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to query processing. Aggregate KPIsmay be defined to provide a measure of service performance calculatedfrom a set of service aspect KPI values; this aggregate may even betaken across defined timeframes and/or across multiple services. Aparticular service may have an aggregate KPI derived from substantiallyall of the aspect KPI's of the service to indicate an overall healthscore for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a query resultset). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.0 Anomaly Detection Based on Relationships Between Multiple TimeSeries

Data may be collected as a time series data set, that is, a sequence ofdata points of a time series, often including successive measurementsmade over a time interval. Each data point may include a correspondingtime stamp and value of the time series. However, in some cases, a timestamp need not be included with a data point. For example, a time seriesmay generally refer to an ordered sequence of time series values. Theordering of values may be based on temporal information (e.g., inmachine data) corresponding to the values. A time series can correspondto one or more metrics, where a metric represents a particularcharacteristic of computer activity. In some cases, each data point in atime series may correspond to a measurement of the particularcharacteristic represented by the metric. Examples of metrics includesecurity-related metrics, industrial metrics (e.g., corresponding todata produced by industrial equipment), business metrics, behavioralmetrics (e.g., corresponding to data produced as a result of actions,using a range of devices connected to the Internet, such as a PC,tablet, or smartphone), transactional metrics (e.g., corresponding todata describing a change as a result of a transaction, and/orperformance metrics.

A time series data set may be determined or derived from machine data.As indicated above, sources of the machine data can include informationprocessing logs, market transactions, and sensor data from real-timemonitors (supply chains, military operation networks, or securitysystems). For example, in some cases, each data point of a time seriesis derived from machine data associated with one or more time stampedrecords, such as events. A value and/or time stamp of a data point maycorrespond to one or more values associated with an event (e.g.,statistics or field values).

It is often desirable to monitor a time series data set in order todetect anomalies in associated values. For example, anomalous or unusualbehavior of a time series can indicate some underlying problem in acomputing system or service associated with the time series. When ananomaly occurs in a time series data set, it is often critical toquickly notify appropriate users or otherwise take appropriate action.For example, an anomaly in a security-related metric could indicate asecurity breach or an anomaly in a performance metric could indicate asystem malfunction. When functioning properly, computer anomalydetection technology allows for these objectives to be met byautomatically monitoring metrics for anomalies and executing appropriateactions when anomalies are detected.

An effective approach to detecting anomalies can leverage relationshipsbetween different time series data sets. In particular, valuesassociated with a first time series data set may historically exhibitsimilar behavior to values associated with a second time series dataset. Thus, values associated with the second time series data setprovide information useful for predicting values associated with thefirst data set. For example, when the values associated with the firsttime series data set sufficiently deviate from predicted values, it mayindicate one of the time series data sets did not behave as expected andtherefore an anomaly may be detected.

To illustrate the forgoing, assume a first metric represents CPU loadrelated to a computing service and a second metric represents bandwidthutilization related to the computing service. Under normalcircumstances, the two metrics may exhibit a cohesive relationship,where their associated values tend to rise and fall together. Adeviation from the cohesive relationship can indicate some underlyingproblem related to the service, and therefore it is effective to detectan anomaly based on the deviation.

In order to accurately detect anomalies, conventional computer anomalydetection technologies require a user to identify particular metricswhich are expected to exhibit a cohesive relationship. It is assumed allof the identified metrics are related to one another by the samecohesive relationship, which may be not true. Additionally, therelationships between the metrics may change over time, such that theymay not always exhibit the cohesive relationship while still behaving inan acceptable manner. Therefore, conventional computer anomaly detectiontechnologies are prone to false positives or negatives in anomalydetection unless these specific criteria are satisfied. Some embodimentsof the present disclosure allow these shortcomings to be remedied.

Aspects of the present disclosure are discussed with respect to FIG. 12,by way of example. FIG. 12 illustrates visualization 1200 of time seriesdata sets in accordance with disclosed embodiments. Visualization 1200is a graphical representation of values associated with time series datasets 1202A, 1202B, and 1202C (collectively 1202), time series data sets1204A, 1204B, and 1204C (collectively 1204), time series data sets 1206Aand 1206B (collectively 1206), time series data set 1208, and timeseries data set 1210. Each time series data set is of a time series,which may correspond to one or more metrics. The data points associatedwith the time series data sets are graphed in visualization 1200, wheretime stamps correspond to the X-axis and values correspond to theY-axis.

Using approaches described herein, a user may optionally identify and/orselect a set of time series and/or time series data sets correspondingto the time series for anomaly detection, such as those shown invisualization 1200. In response to or based on a user selection or otheruser interaction associated with the time series data sets, a search isperformed on the time series data sets to automatically determine andidentify one or more relationships, if any, between any combination ofthe time series data sets.

Unlike conventional approaches, in various embodiments, all of the timeseries data sets need not have a cohesive relationship with one anotherfor anomaly detection to function properly. Also, the search can covermany types of relationships between time series data sets and is notlimited to cohesive relationships. For example, the search can identifytime series data sets 1202 having a cohesive relationship with oneanother, time series data sets 1206 having a cohesive relationship withone another, time series data sets 1204 having a time-shiftedrelationship with each of time series data sets 1202, time series dataset 1208 having an anti-correlated relationship with time series datasets 1206 (i.e., as one rises, the other falls and as one falls, theother rises), and time series 1210 which is unrelated to any othersshown in FIG. 12.

In some aspects of the present disclosure, relationships between timeseries data sets are automatically determined by analyzing valuesassociated with the time series data sets. Each relationship is capturedby a predictive model, which is configured to generate predicted valuesassociated with at least one time series using values from at least oneother time series. A predictive model can be a machine learning modelwhich is trained to predict future values (e.g., data points) associatedwith a time series given corresponding values (e.g., data points) of atleast one other time series based on prior behavior of the multiple timeseries, such as the behavior represented in FIG. 12. Anomalies aredetected based on identifying deviations between the predicted valuesand actual values associated with the multiple time series.

To illustrate the forgoing, a predictive model could predict values oftime series 1202A that increase from previous values based on actualvalues in time series data sets 1202B and 1202C increasing due to thecohesive relationship. If it is determined at least one of thecorresponding actual values of time series data set 1202A issufficiently different than at least one of the predicted values (e.g.,based on a threshold relative to a predicted value being exceeded by anactual value), an anomaly may be detected. For example, the values couldfall when they were predicted to rise, or not rise by a sufficientamount.

In some implementations, in order to search for and select a predictivemodel to use for anomaly detection on values associated with a timeseries (e.g., at least one time series), a plurality of predictivemodels is generated for the time series. Each predictive model can beconfigured to independently predict the values associated with the timeseries using values associated with at least one other time series. Forexample, one predictive model may predict values of time series data set1202A based on values from time series data set 1202B (e.g., they may beexplanatory variables), another predictive model may be similar, but usea different predictor function, and another may predict values of timeseries data set 1202A based on values from both time series data sets1202B and 1202C. A predictive model is selected from the generatedmodels for the anomaly detection based on an evaluation of the predictedvalues produced by the model. For example, the most accurate predictivemodel may be selected for anomaly detection, or no predictive model maybe selected if none are sufficiently accurate.

3.1 Anomaly Detection Tool in a Data Processing Environment

FIG. 13 illustrates an example of a data processing environment in whichan anomaly detection tool may be employed in accordance with variousembodiments of the present disclosure. However, it will be appreciatedthat many variations are possible. Generally, data processingenvironment 1300 provides for, or enables, the management, storage, andretrieval of data to determine relationships between time series datasets and detect anomalies using those determined relationships.

As shown in FIG. 13, data processing environment 1300 includes anomalydetection tool 1316 used to determine relationships between time seriesdata sets and detect anomalies using those determined relationships.Anomaly detection tool 1316 can utilize historical time series data setsto generate predictive models which predict values of time series datasets using values of other time series data sets. Anomaly detection tool1316 can further detect anomalies in time series data sets based onevaluating predicted values from their associated predictive models withrespect to actual values associated with the time series data sets.

Anomaly detection tool 1316 can, in some cases, be integrated intocomponents of the various systems and environments described above, suchas those depicted in FIGS. 1, 2, 10, and 11. For example, client device1304 can correspond to client device 1002, client devices 102, or clientdevices 1104; data source 1306 can correspond to data sources 202;indexer 1312 can correspond to indexers 206; data store 1314 cancorrespond to data stores 208; and network 1308 can correspond to anycombination of networks 104, 1004, and 1120 described above.

Data processing system 1302 is communicatively coupled to one or moreclient devices 1304 and one or more data sources 1306 via acommunications network 1308. Network 1308 may include an element orsystem that facilitates communication between the entities of dataprocessing environment 1300. Network 1308 may include an electroniccommunications network, such as the Internet, a local area network(LAN), a wide area network (WAN), a wireless local area network (WLAN),a cellular communications network, and/or the like. In some embodiments,network 1308 includes a wired or a wireless network. In someembodiments, network 1308 includes a single network or a combination ofnetworks.

Data source 1306 may be at least one source of incoming source data 1310being fed into the data processing system 1302. Data source 1306 can beor include one or more external data sources, such as web servers,application servers, databases, firewalls, routers, operating systems,and software applications that execute on computer systems, mobiledevices, sensors, and/or the like. Data source 1306 may be locatedremote from data processing system 1302. For example, data source 1306may be defined on an agent computer operating remote from dataprocessing system 1302, such as on-site at a customer's location, thattransmits source data 1310 to data processing system 1302 via acommunications network (e.g., network 1308).

Source data 1310 can be a stream or set of data fed to an entity of dataprocessing system 1302, such as a forwarder (e.g., forwarder 204) orindexer 1312. In some embodiments, source data 1310 can be heterogeneousmachine-generated data received from various data sources, such asservers, databases, applications, networks, and/or the like. Source data1310 may include, for example raw data (e.g., raw time-series data),such as server log files, activity log files, configuration files,messages, network packet data, performance measurements, sensormeasurements, and/or the like. For example, source data 1310 may includelog data generated by a server during the normal course of operation(e.g., server log data). In some embodiments, source data 1310 may beminimally processed to generate minimally processed source data. Forexample, source data 1310 may be received from data source 1306, such asa server. Source data 1310 may then be subjected to a small amount ofprocessing to break the data into events. As discussed, an eventgenerally refers to a portion, or a segment of the data, that isassociated with a time. The resulting events may optionally be indexed(e.g., stored in a raw data file associated with an index file). In someembodiments, indexing source data 1310 may include additionalprocessing, such as compression, replication, and/or the like.

Source data 1310 might be structured data or unstructured data.Structured data has a predefined format, wherein specific data itemswith specific data formats reside at predefined locations in the data.For example, data contained in relational databases and spreadsheets maybe structured data sets. In contrast, unstructured data does not have apredefined format. This means that unstructured data can comprisevarious data items having different data types that can reside atdifferent locations.

Indexer 1312 of data processing system 1302, when present, may receivesource data 1310, for example, from a forwarder (not shown in FIG. 13)or data source 1306, and apportion source data 1310 into events. Indexer1312 may be an entity of data processing system 1302 that indexes data,transforming source data 1310 into events and placing the results intodata store 1314 (e.g., and index). Indexer 1312 may also search datastores 1314 in response to requests or queries. Indexer 1312 may performother functions, such as data input and search management.

During indexing, and at a high-level, indexer 1312 can facilitate takingdata from its origin in sources, such as log files and network feeds, toits transformation into searchable events that encapsulate valuableknowledge. Indexer 1312 may acquire a raw data stream (e.g., source data1310) from its source (e.g., data source 1306), break it into blocks(e.g., 64K blocks of data), and/or annotate each block with metadatakeys. After the data has been input, the data can be parsed. This caninclude, for example, identifying event boundaries, identifying eventtimestamps (or creating them if they don't exist), masking sensitiveevent data (such as credit card or social security numbers), applyingcustom metadata to incoming events, and/or the like. Accordingly, theraw data may be data broken into individual events. The parsed data(also referred to as “events”) may be written to a data store, such asan index or data store 1314.

Data store 1314 (e.g., data store 208) may include a medium for thestorage of data thereon. For example, data store 1314 may includenon-transitory computer-readable medium storing data thereon that isaccessible by entities of data processing environment 1300, such asindexer 1312 and anomaly detection tool 1316. As can be appreciated,data store 1314 may store the data (e.g., events) in any manner. In someimplementations, the data may include one or more indexes including oneor more buckets, and the buckets may include an index file and/or rawdata file (e.g., including parsed, time-stamped events). In someembodiments, each data store is managed by a given indexer that storesdata to the data store and/or performs searches of the data stored onthe data store. Although a single data store 1314 could be employed,embodiments may include multiple data stores, such as a plurality ofdistributed data stores.

Events within data store 1314 may be represented by a data structurethat is associated with a certain point in time and includes a portionof raw machine data (e.g., a portion of machine-generated data that hasnot been manipulated). An event may include, for example, a line of datathat includes a time reference (e.g., a timestamp), and one or moreother values. In some embodiments, events can correspond to data that isgenerated on a regular basis and/or in response to the occurrence of agiven event. In the context of server log data, for example, a serverthat logs activity every second may generate a log entry every second,and the log entries may be stored as corresponding events of the sourcedata. Similarly, a server that logs data upon the occurrence of an errorevent may generate a log entry each time an error occurs, and the logentries may be stored as corresponding events of the source data.

In accordance with some embodiments, events within data store 1314 cancorresponds to time series data sets analyzed and/or monitored byanomaly detection tool 1316, such as time series data sets shown in FIG.12. For example, in some cases, time stamps of data points maycorrespond to time stamps associated with one or more of events andvalues of data points may correspond to field values associated with theone or more events (e.g., extracted from the portions of machine data ofthe events). For a time series, a value of a data point may correspondto ones or more fields values from the same set of fields for eachevent. Further, as described above, a field may be defined by anextraction rule which extracts the field values from the events. Also,an extraction rule may be defined by a query on events. Thus, a timeseries may correspond to a search on the events. Further, the search maybe ongoing as data is ingested by data processing environment 1300resulting in new data points being generated for the time series datasets.

Although anomaly detection tool 1316 is illustrated and described hereinas a separate component, this is for illustrative purposes. Anomalydetection tool 1316 or functions described in association therewith, canbe performed at indexer 1312, a search head (not shown), or any othercomponent of data processing environment 1300. For example, somefunctionality described in association with anomaly detection tool 1316might be performed at a search head, while other functionality describedin association with anomaly detection tool 1316 might be performed at anindexer. Also, in various embodiments, the time series data setsavailable to anomaly detection tool 1316 need not correspond to events,and can be generated and stored in any suitable manner.

As indicated above, anomaly detection tool 1316 may optionally beinitiated by a user of client device 1304. For example, client device1304 may be used or otherwise accessed by a user 1322, such as a systemadministrator or a customer. Client device 1304 may include any varietyof electronic devices. In some embodiments, client device 1304 caninclude a device capable of communicating information via network 1308.Client device 1304 may include one or more computer devices, such as adesktop computer, a server, a laptop computer, a tablet computer, awearable computer device, a personal digital assistant (PDA), a smartphone, and/or the like. In some embodiments, a client device 1304 may bea client of event processing system 1302. Client device 1304 can includevarious input/output (I/O) interfaces, such as a display (e.g., fordisplaying a graphical user interface (GUI), an audible output userinterface (e.g., a speaker), an audible input user interface (e.g., amicrophone), an image acquisition interface (e.g., a camera), akeyboard, a pointer/selection device (e.g., a mouse, a trackball, atouchpad, a touchscreen, a gesture capture or detecting device, or astylus), and/or the like.

In some embodiments, client device 1304 can include general computingcomponents and/or embedded systems optimized with specific componentsfor performing specific tasks. In some embodiments, client device 1304can include programs/applications that can be used to generate a requestfor content, to provide content, to render content, and/or to sendand/or receive requests to and/or from other devices via network 1308.For example, client device 1304 may include an Internet browserapplication that facilitates communication with data processing system1302 via network 1308. In some embodiments, a program, or application,of client device 1304 can include program modules having programinstructions that are executable by a computer system to perform some orall of the functionality described herein with regard to at least clientdevice 1304.

Anomaly detection can be initiated, triggered, and/or configured byinstructions from client device 1304, which may be user provided via agraphical user interface (GUI). In some embodiments, data processingsystem 1302 provides the display of the GUI. Such a GUI can be displayedon client device 1304, and can present information relating toinitiating, performing, and viewing results of anomaly detection and/orpredictive model generation, and/or configuring settings and parametersof any of the forgoing.

Anomaly detection and model generation can be performed upon eventsbeing created, indexed, and stored, or before or as events are created,indexed, and/or stored. Further, these functions may be automaticallytriggered, possibly without user initiation. In some cases, uponinitially establishing anomaly detection, subsequent prediction analysesmay be automatically triggered and performed as new data is received.Further, predictive models used in anomaly detection may be updated overtime, such as based on an analysis of subsequently created, indexed,and/or stored events or data points.

3.2 Generation and Selection of Predictive Models

As described above, a set of time series data sets can be identified orselected for anomaly detection and/or predictive model generation by auser or otherwise. In response, predictive models can be generatedand/or selected for one or more time series corresponding to the set oftime series data sets. For example, for a given time seriescorresponding to the set of time series data sets, anomaly detectiontool 1316 can attempt to generate and select a predictive model foranomaly detection. This may be performed by a search process where manypredictive models are generated for the same time series, the accuracyor suitability of the various predictive models are analyzed andevaluated, and at least one of the predictive models is selected for usein anomaly detection associated with the time series. An example of sucha process is shown and described with respect to FIG. 14A.

FIG. 14A illustrates an example of generating and selecting predictivemodels for a time series, in accordance with embodiments of the presentdisclosure. As shown in FIG. 14A, predictive models 1410 can begenerated for a given time series. Selected model 1416, which is asubset of predictive models 1410 is selected from predictive models 1410for use in anomaly detection based on an analysis of outputs (e.g.,predicted values) from predictive models 1410. As will later bedescribed in additional detail, generating and selecting predictivemodels can optionally be an iterative process where in each iteration,predictive models 1410 are evaluated and narrowed until one or moremodels (e.g., selected model 1416) is selected for anomaly detectionassociated with the time series. In the example shown, predictive models1410 are narrowed to predictive models 1412 in one iteration, predictivemodels 1412 are narrowed to predictive models 1414 in a subsequentiteration, and predictive models 1414 are narrowed to selected model1416 in a subsequent iteration.

Each predictive model 1410 for the time series is generated fromsequences of time series values, such as those corresponding to datapoints of any combination of the time series data sets shown in FIG. 12.The predictive models can be machine learning models, which learn fromthe sequences of time series values to generation predicted valuesassociated with the time series. In particular, each predictive modelmay learn to generate predicted values associated with the time seriesusing values of at least one other time series.

FIG. 14B illustrates a diagram used to describe training and evaluatinga predictive model in accordance with some embodiments of the presentdisclosure. The predictive model can be any of predictive models 1410.The predictive model is trained using values of time series 1420, timeseries 1422, and time series 1430 from training period 1440. Inparticular, the predictive model uses values of those time series tolearn to predict values of time series 1430 from values of time series1420 and time series 1422. Although this example is for predictingvalues of time series 1430 from values of two other time series, itshould be appreciated that values of any number of time series may beused to learn and predict the time series (e.g., one or more) anddifferent numbers of time series and different time series may be usedfor different ones of predictive models 1410. Typically, each timeseries used to predict another time series is selected from the set oftime series identified for anomaly detection and/or predictive modelgeneration. Selection of these time series will later be described infurther detail.

The training of the predictive model can use supervised learning wherevalues from the multiple time series are used to generate trainingexamples and the predictive model uses the training examples to infer afunction for predicting the values associated with time series 1430 fromtime series 1420 and time series 1422 (or whichever time series are usedfor prediction). Examples of suitable types of predictive models areshown in FIG. 14A. For example, any number of and combination of binarymodels 1410A, linear models 1410B, polynomial models 1410C, tree models1410B, and neural network models 1410E may be employed. It should beappreciated that all models may be of the same type (e.g., allpolynomial), or different types may be used as in the example shown.Different model types have different capabilities of accuratelypredicting values of a time series depending on the characteristics ofthe training data. For example, some types of models may be better atpredicting certain types of relationships between time series, or otherqualities or characteristics of the data. By using many different typesmodels, the most suitable model for the training data may be determined.

Although any suitable supervised learning models may be used, oneexample of a suitable predictive model is a lasso linear model. Themodel may use iterative fitting along a regularization path. Anotherexample of a suitable predictive model is a decision tree regressor. Afurther example of a suitable predictive model is a random forestsregressor. This model may be a meta estimator that fits a number ofclassifying decision trees on various sub-samples of the dataset. Themodel may use averaging to improve the predictive accuracy and controlover-fitting.

3.2.1 Features of Predictive Models

Each predictive model may use data from one or more data sources to makeits predictions. For example, a single predictive model could generatepredictions from any combination of one or more metrics, event streams,series of time stamps, and data derived from any of the forgoing, fromone or more data sources. Features of the predictive models may includevalues from the multiple time series. However, many types of featuresmay be employed in any suitable combination. These features may captureexpected or typical types of relationships observed between time series.In various implementations, these features may be added to one to alldata points of the time series used to generate the predictive model.For example, a data point corresponding to an event at a given time maybe enriched with fields or features that correspond to the time seriesat one or more other times, such as five minutes ago, ten minutes ago,etc. In should be appreciated that many different data sources and typesof information can be used to enrich a data point of a time series.

By using additional features, the relationships between time series maybe more readily identified. Furthermore, the accuracy of anomalydetection may be improved by allowing for the capture of additionalcharacteristics, or behavior, in the time series (e.g., this time serieshas a spike at 5 PM every day so it should not be considered anomalous).Further, as described below, these additional characteristics mayprovide additional information that can be conveyed to users inexplanatory messages presented in association with detected anomalies.

In order to capture temporal relationships (e.g., time-shifted), one ormore temporal features may be used. Examples of temporal featuresinclude a time of day, day of the week, periodic based features of thevalues, and more. Using temporal features, the predictive models mayfactor in historical values of the time series in addition to currentvalues. Thus, time-shifted relationships may be more readily identified.Other examples of features are aggregated value features, which combinevalues of a time series into a feature of a predictive model. An exampleof an aggregated value feature is a sliding window sum of values.Another example is a turbulence feature (e.g., value), which quantifiesthe turbulence observed in a time series over a window or period oftime.

In some implementations, anomaly detection tool 1316 analyzes the timesseries' and engineer's features for the predictive models based on theanalysis. For example, anomaly detection tool 1316 may analyze the timeseries to determine whether one or more particular features should beincluded in the predictive models. Based one the determination, thepredictive models may be generated using the features, examples of whichhave been described above. Doing so can reduce processing needed forgenerating and selecting predictive models by including features likelyto be relevant to the time series while excluding other features whichare unlikely to be relevant.

3.2.2 Evaluation of Predictive Models

As indicated in FIG. 14B, in some implementations, predictive models aretrained over a training period, such as training period 1440. Eachtraining period 1440 can be, for example, at least one hour and usesequences of time series values from time series 1420, time series 1422,and time series 1430 within the training period for training. Asmentioned above, in some cases, the time series values can correspond toone or more streams of source data, and the time series values may beextracted from the source data and used for training over trainingperiod 1440. By optionally training the predictive models as the sourcedata is received, the models can reflect the current characteristics ofthe multiple time series.

As indicated in FIG. 14B, in some implementations, predictive models areevaluated over a predicting period, such as predicting period 1442. Eachpredicting period 1442 can be, for example, at least one hour and usesequences of time series values from time series 1420, time series 1422,and time series 1430 within the prediction period for evaluating thepredictive model. As indicated, a predicting period is typically shorterthan a training period. Similar to training period 1440, the time seriesvalues can correspond to the one or more streams of source data, and maybe extracted from the source data and used for evaluating predictivemodels over predicting period 1442. As shown, the time series valuesused for predicting periods may be different values than those used fortraining and may follow the values used for training in the sequences oftime series values.

Evaluating a predictive model can be based on determining an errorbetween predicted values generated by the predictive model and actualvalues (e.g., what the values of time series 1430 actually were at thosetimes) corresponding to those predicted values. In some implementations,the error corresponds to a residual between a predicted value and anactual value of the time series. For example, as shown, the predictivemodel generates predicted time series 1432 based on values of timeseries 1420 and time series 1422 within predicting period 1442. Althoughnot shown, predicted time series 1432 may be generated within trainingperiod 1440 and could be used for training. Residual time series 1434 isdetermined from actual values of time series 1430 and correspondingpredicted values of predicted time series 1432 within predicting period1442. The residuals represent differences between time series 1430 andpredicted time series 1432 and therefore indicate the accuracy of thepredictive model. For example, in general, the smaller the residuals,the more accurate the predictive model. Therefore, the residuals canserve as an effective basis for evaluating the predictive model foraccuracy.

As mentioned above, the predictive model may be selected for anomalydetection based on the evaluation. This selection can be based, in parton the determined error (e.g., residual or one or more values derivedfrom the residual) of the model. For example, a model may be selectedbased on the error failing to exceed a threshold value and/or range ofthreshold values. Referring to FIG. 14B, errors may be determined foreach of predictive models 1410 in order to select a subset of themodels. For example, the lower a predictive model's error, the morelikely it will be selected for anomaly detection. In some cases, theerror of each model may correspond to the same prediction period 1442.Also, each model may be trained using the same training period 1440.

Other factors may be considered in selecting a predictive model foranomaly detection in addition to or instead of the error, or accuracy,of the model. In general, a model may be selected based on one or moreof the characteristics of the model, whether it be based on it's modeltype, accuracy of the model, or non-accuracy related characteristics(e.g., based on evaluating the complexity of the model). In variousimplementations, one model may be selected over one or more other modelsbased on a comparison between or evaluation of one or morecharacteristics of the model and/or the other model(s).

In some cases, one or more models may be selected using one or moreheuristics based on characteristics of the model and/or other models. Asan example, a model may be selected based on the explanatory power ofthe model and/or other models being considered for selection. Toillustrate the forging, if a model of a decision tree type (or otherdefined type) is less than a defined number (e.g., 3) of levels deep,and the accuracy of the model is no more than a defined percentage(e.g., 5%) lower than at least a second model or model type (e.g., aneural network), the model may be selected.

A predictive model may be selected based on the model's explanatoryvalue. The explanatory value of a predication model can correspond tothe relative ease of conveying the relationship captured by the model toa user. As an example, polynomial models will generally have higherexplanatory value than a neural network because the function representedby a polynomial model can be presented more concisely or legibly thanthe function represented by a neural network. Thus, explanatory value ofa predictive model may be based on the length of its prediction formulaor other complexity related characteristics of the model.

In some cases, explanatory values may be assigned to each predictivemodel type, and may be preassigned and predetermined prior to trainingor prediction. As another example, explanatory value could be determinedbased on analyzing the complexity of the model. More complex models maybe considered to have lower explanatory power. Also, more complex modelsmay be more computationally or otherwise resource intensive to useand/or store. Thus, a model may be selected based on evaluating resourcerequirements associated with the model and/or other models.

From the forgoing, it will be appreciated that any number of predictivemodels (e.g., selected model 1416) may be selected for anomaly detectionand/or continued training and evaluation based on many differentpotential characteristics of one or more models being considered.Further, where a subset of predictive models is selected multiple times(e.g., in narrowing the models) different characteristics and criteriacould be used for different subset selections.

Also from the forgoing, it should be appreciated that in selecting asubset of predictive models 1410, one model may be selected over anotherdespite having higher error based on other factors, such as its higherexplanatory value, or other characteristics, such as by usingheuristics. These characteristics may be evaluated in any suitablemanner including weighting each factor of a predictive model andcombining the weighted factors into an evaluation score for thepredictive model and/or using conditions or rules based on modelcharacteristics. It is noted that some predictive models may be screenedout from selection based on determining their error is sufficientlyhigh. In some cases, predictive models 1410 may be filtered based ontheir error, and one or more remaining models may be selected based ontheir explanatory values or other characteristics.

As mentioned above, generating and selecting predictive models canoptionally be an iterative process where in each iteration, predictivemodels 1410 are evaluated and narrowed until one or more models (e.g.,selected model 1416) is selected for anomaly detection associated withthe time series. As indicated above, in these cases, the evaluatingdescribed above may be used for selecting the narrower subset ofpredictive models. Training may continue for the selected subset ofpredictive models, whereas the unselected models may be filtered outfrom training and evaluation. For example, a subsequent training period1440 and predicting period 1442 may be used for each iteration. Inembodiments where models are filtered out, as shown, computing resourcesare preserved by retaining only the most promising predictive models.

It is noted that in some embodiments, training and/or evaluatingpredictive models continues or otherwise occurs after the predictivemodels are selected for anomaly detection, such as during anomalydetection. For example, incoming data which is analyzed for anomaliesmay also be used to train and/or evaluate selected predictive models.Continued training of predictive models allow the predictive models toadapt to changes in the behavior of the data. Further, continuedevaluation allows for tracking accuracies or errors of the models overtime. In some cases, based on an error of a model used for anomalydetection, the anomaly detection tool may attempt to generate and selecta new predictive model for anomaly detection. This could be accomplishedsimilar to or different than what has been described with respect toFIGS. 14A and 14B. In addition, or instead, the anomaly detection toolmay cause an indicator or notification of the error to be transmitted toa user or user device.

3.2.3 Approximation Mining

In some implementations, predictive models are exhaustively generatedfor each identified time series and combination of parameters and modeltypes. This approach may be referred to as an exhaustive search forpredictive models. In other cases, one or more heuristics are used toperform a directed search. Directed searches may use heuristics toprefer one more model type for metrics and another for events orotherwise factor particular model types for time series determined tohave one or more designated characteristics. Some approaches may usegradient descent to reduce the search space.

Various approaches to direct search include approximation mining, whichattempts to reduce the number of predictive models generated andselected from for a set of time series. Without approximation mining,given a set of time series, anomaly detection tool 1316 could attempt togenerate and determine at least one predictive model to predict eachtime series. Furthermore, for a given time series, a predictive modelcould be generated and trained for each combination of predictive modeltype and time series used to predict the time series. While thisapproach may be practical when the set of time series and the number ofpredictive model types is small, as these variables rise, thecomputational resources needed to generate and select predictive modelsquickly becomes untenable.

In some implementations, anomaly detection tool 1316 performs clusteringon the set of time series. In clustering, anomaly detection tool 1316analyzes the multiple time series in order to group the time series intosubsets. Rather than attempting to generate and select predictive modelsfor each time series, anomaly detection tool 1316 may perform thisprocess for each subset. In some cases, anomaly detection tool 1316down-samples each time series in the set, normalizes the various timeseries, and smooths each time series (e.g., using a rolling median).Anomaly detection tool 1316 may then apply a clustering algorithm (e.g.,a k-means algorithm) to the processed time series to cluster the timeseries.

A representative time series may be extracted from each cluster (e.g.,where each value is the average value of the multiple time series for agiven time). Anomaly detection tool 1316 may then perform predictivemodel selection and generation using the composite time series from eachcluster. For example, each predictive model may attempt to predict agiven composite time series from at least one other composite timeseries. When performing anomaly detection on a time series in theinitial set of time series, anomaly detection tool 1316 may use thepredictive model selected for its associated cluster.

In addition, or instead, anomaly detection tool 1316 may use a divideand conquer approach to approximation mining. Given a set of time series(e.g., initial time series or representative time series of clusters),anomaly detection tool 1316 selects a reference time series (e.g.,randomly). Of the remaining set, anomaly detection tool 1316 selects aset of predictor time series to use as predictors of the reference timeseries (e.g., a random set). Anomaly detection tool 1316 uses thereference time series and the set of predictor time series to generatepredictive models and attempt to select a suitable predictive model forthe reference time series, as described above. If successful, thereference time series and set of predictor time series is retained as afamily and the process repeats with the reduced set of time series. Ifunsuccessful, the process may repeat with the previous set of timeseries. This can repeat until each time series is a member of a family.It is noted, a family may only include a reference time series ifanomaly detection tool 1316 determines there is no remaining time seriesavailable for producing a suitable predictive model for the time series(e.g., by analyzing generated predictive models associated with thereference time series). This may be based on attempting and failing tofind a family for the reference time series a threshold number of times.

Anomaly detection tool 1316 can then select a member of each family(e.g., a random member). Using the set of selected time series from thefamilies, anomaly detection tool 1316 may repeat the aforementionedprocess described for the initial set of time series. This can result innew sets of families. Anomaly detection tool 1316 then merges the newfamily of each time series with the previously generated family of thetime series. Anomaly detection tool 1316 may repeat the selection familymembers, generation of new families, and merging of families, any numberof times until one or more ending conditions are satisfied. An exampleof an ending condition is when each family with at least two members hasgreater than a threshold number of members, and when anomaly detectiontool 1316 has attempted and failed to find family members for eachremaining time series a threshold number of times. The predictive modelsfrom each family may then be used for anomaly detection associated withtheir corresponding members.

3.3 Anomaly Detection Using Predictive Models

The predictive model(s) for each time series can be used to detectanomalies in values associated with the time series. Many approaches areavailable for anomaly detection using predicted values from thepredictive models and actual values associated with the multiple timeseries. In one approach, the anomaly detection tool determines themedian absolute deviation (MAD) from the predicted and actual valuesover a moving window of time. An anomaly may be detected based on theMAD exceeding a threshold value (e.g., falling outside of a range ofvalues). The threshold may be a multiple ‘w’ of the MAD, such that thethreshold is w*MAD.

Some approaches to anomaly detection are based on determining andcomparing characteristics of values of the time series being predicted,the predicted values of the time series, and/or the time series uponwhich the predicted value are based. As one example, an anomaly may bedetected, at least in part, based on determining turbulence of thevalues in the various time series. For example, anomaly detection tool1316 could determine a turbulence of values in the predicted timeseries, which is compared to the actual turbulence of values in the timeseries.

As indicated above, any number of predictive models may be selected foranomaly detection (e.g., selected model 1416). Multiple predictivemodels can be used in various ways for the anomaly detection. Forexample, predictions from one or more of the models may be combinedusing some function (e.g., mean or max) and the combined values may beused for anomaly detection. For example, the combined values could beanalyzed for anomalies therein, be used to analyze a time series foranomalies (e.g., one of the multiple models or another model), used tovalidate anomaly detection, or for other purposes. In some cases,predictions from at least one predictive model are used to validatepredictions from one or more other predictive models. This could beused, for example, to detect concept drift in a predictive model.

There are many potential approaches for determining whether aresidual(s) is sufficiently large to consider it an anomaly. Anycombination of input parameters, user roles, configuration settings,residuals of other models around the same time, or temporal propertiesof the residual could be employed (e.g., take the local integral toconsider intensity and duration). With respect to user roles, forexample, some user roles in the system could be associated with higherthresholds than others for anomaly detection. As an example, aninformation technology Ops analyst may be tasked with reviewing alertsand may want a lower threshold than another role, such as a boss ormanager, who may use a higher threshold so as to be notified of moresevere incidents. These thresholds may be saved in association with theuser roles and used for anomaly detection associated with acorresponding user role. Further, a user could provide an inputparameter or configuration file setting various anomaly detectionparameters, such as detection thresholds.

The anomaly detection tool may cause transmission of an indication of ananomaly detected using the selected predictive model (e.g., to a userand/or user device). For example, the anomaly detection tool can causethe indication to be automatically transmitted in response to detectingone or more anomalies (e.g., as an alert). The indication may take avariety of forms and many indications may be caused to be transmittedfor a particular anomaly or particular anomalies. For example, anindication may be transmitted as part of an email, push notification,phone message, or display screen. As another example, an indicationcould be stored, retained, and/or analyzed to trigger actions such asretaining and/or generating of predictive models.

FIG. 15. illustrates a display including examples of indications ofanomalies, in accordance with embodiments of the present disclosure.Display 1500 may be provided for presentation on a user device, such asa client device (e.g., client device 1304). For example, anomalydetection tool 1316 can cause display 1500 to be presented on the userdevice. Display 1500 includes a graphical display of time series 1530and time series 1540 from which anomaly detection tool 1316 has detectedanomalies.

Display 1500 includes indications 1502 of anomalies detected by anomalydetection tool 1316 using a selected predictive model, such as selectedmodel 1416. In this example, each indication corresponds to a detectedanomalous value. The detected anomalous value is indicated on thegraphical display of time series 1530 (e.g., at its relative position inthe time series). Display 1500 also includes indications 1512, 1514, and1516, which each correspond to multiple detected anomalous values. Thedetected anomalous values are indicated on the graphical display of timeseries 1530.

Also included is explanatory message (e.g., a verbal explanation such astext as a string and/or one or more sentences) generated based on timeseries 1530 and explanatory message 1522 generated based on time series1540. Each explanatory message may indicate the expected or predictedrelationship between the time series used for the anomaly detection. Forexample, an explanatory message could indicate there was an expectedcohesive, anti-correlated, or time-shifted relationship between the timeseries captured by the predictive model. The explanatory message mayalso indicate other expected characteristics, such as the magnitude ofthe expected relationship, turbulence, and the like. The expectedbehavior may correspond to an observed characteristic determined fromthe multiple time series. An anomaly may optionally be detected based onthe observed characteristic. Further, the observed characteristic orrelationship may be presented to in the explanatory message (e.g., withthe expected characteristic). An explanatory message may be included ina dashboard panel, a new event, or other knowledge object of the system.

As an example, a message could read “Normally metric A is X times metricB, but it was observed as Y times metric B,” where X is a numberrepresenting the predicted characteristic and Y is a number representingthe observed characteristic. Another example includes “Normally metric Ais time-shifted by metrics B and C by X hours, but it was observed asbeing time-shifted by Y hours.” Further examples include “The usualanti-correlated relationship between metrics A and B have beenviolated.” It should be appreciated that many different variations arepossible. Further, predicted characteristics are generally determinedusing the predictive model(s) used for anomaly detection (e.g.,corresponding to parameters of the prediction function).

It is noted that selected predictive models (e.g., selected model 1416)can be used and selected for many purposes, in addition to or instead offor anomaly detection. As an example, one or more predictive models canbe used for imputation of missing values in a time series. Values may bemissing or inaccurate in a time series based on network or serviceinterruptions, system crashes, or broken, disabled, or malfunctioningsensors or other computing components used to generate the time series.Predicted values from one or more predictive models can be used tovalidate and/or modify (e.g., change values of or add values to) thetime series and/or event stream corresponding to the time series.

In some implementations, triaging is applied to at least one detectedanomaly to determine whether the at least one anomaly is a falsepositive or negative. For example, based on the at least one anomalybeing detected, further actions and determinations may be performed inorder to validate the anomaly. If an anomaly is validated, an alert maybe transmitted or other remedial action(s) may be performed. However, ifthe anomaly is invalidated, the remedial action may not be performed. Asan example, assume an anomaly indicates a sensor reported a temperatureof 500 degrees C. This could mean there is a fire and a fire alarm orother device should be triggered. However, this could also mean therewas an error in the reporting. Using triaging, the anomaly could betransmitted to an automated system which uses a camera to determinewhether to triggering a fire alarm, a maintenance ticket for the sensor,or take no remedial action.

As further examples, predictive errors (e.g., residuals) could be usedas time series and incorporated into visualizations, such as dashboards,examples of which have been described above. This could allow forobservation of the general quality of predictive models and/or a view ofsystem behavior.

In some cases, one or more predictive models could be generated andselected from using methods described above, for each data center of aplurality of data centers using source data from the corresponding datasources (e.g., to predict the same metric or time series based on thedata available at the data center). The resultant predictions from eachdata center could be combined and one or more predictive models could begenerated from the combined predictions (e.g., in a time series) andselected from using methods described above to model inter-data centerrelationships.

Predictive models may also be utilized for forecasting the impact ofchanges to one of more time series on one or more other time series. Forexample, assume a predictive model captures the relationship between aCPU load metric and a network traffic metric. By modifying the networktraffic (e.g., increasing the traffic by 10%), the impact of themodification can be forecasted for the CPU load. Using a forecasted timeseries, one or more actions could be automatically taken, such asallocating, modifying, configuring, or adjusting system resources orservices corresponding to the metrics based on the forecasted timeseries.

3.4 Illustrative Examples

FIGS. 16-18 illustrate various methods in accordance with embodiments ofthe present disclosure. Although the methods are provided as separatemethods, aspects thereof may be combined into a single method orcombination of methods. As can be appreciated, additional or alternativesteps may also be included in different embodiments.

With initial reference to FIG. 16, FIG. 16 illustrates method 1600 inaccordance with embodiments of the present application. Such a methodmay be performed, for example, at an anomaly detection tool, such asanomaly detection tool 1316 of FIG. 13. At block 1602, sequences of timeseries values are obtained. For example, anomaly detection tool 1316 mayobtain sequences of time series values from data store 1314 and/orindexer 1312. The time series values may correspond to events and may bedetermined from machine data. Further, each sequence can correspond to arespective time series. The sequences may or may not correspond to oneor more streams of source data.

At block 1604, a plurality of predictive models is generated for a firsttime series from the sequences. For example, anomaly detection tool 1316can generate predictive models 1410 for a first time series from thesequences of time series values. Each predictive model is configured togenerate predicted values (e.g., corresponding to predicted time series1432) associated with the first time series (e.g., time series 1430)using values of or associated with at least one other second time series(e.g., time series 1420 and/or 1422).

At block 1606, errors of the predictive models are determined. Forexample, anomaly detection tool 1316 can for each of the plurality ofpredictive models, determine an error between the correspondingpredicted values and values associated with the first time series. Insome cases, the error can correspond to residuals (e.g., residual timeseries 1434) between the predicted values and the values associated withthe first time series.

At block 1608, a predictive model is selected for anomaly detection. Forexample, anomaly detection tool 1316 can select at least one predictivemodel (e.g., selected model 1416) for anomaly detection based on thedetermined error of the predictive model. In some embodiments, this mayinclude iterative training, evaluating, and filtering of predictivemodels.

At block 1610, transmission is caused of an indication of an anomalydetected by the anomaly detection. For example, anomaly detection tool1316 can cause transmission of an indication of an anomaly detectedusing the selected predictive model. In some cases, the indication maycorrespond to indications 1502, 1512, 1514, or 1516 of FIG. 15.

Turning now to FIG. 17, FIG. 17 illustrates method 1700 in accordancewith embodiments of the present application. Such a method may beperformed, for example, at an anomaly detection tool, such as anomalydetection tool 1316 of FIG. 13. At block 1702, sequences of time seriesvalues are obtained. For example, anomaly detection tool 1316 may obtainsequences of time series values from data store 1314 and/or indexer1312. The time series values may correspond to events and may bedetermined from machine data. Further, each sequence can correspond to arespective time series. The sequences may or may not correspond to oneor more streams of source data.

At block 1704, a set of predictive models for a first timer series istrained from the sequences. For example, anomaly detection tool 1316 maytrain each of predictive models 1412 to predict values of time series1430 using values associated with at least one other time series.

At block 1706, the trained set of predictive models is evaluated. Forexample, anomaly detection tool 1316 may evaluate predicted valuesgenerated using predictive models 1412 with respect to actual values oftime series 1430. This may include determining at least one residual foreach predictive model, such as residual time series 1434.

At block 1708, at least one predictive model is removed from the set.For example, anomaly detection tool 1316 may remove, or filter out atleast one of predictive models 1410 based on the evaluation. Variousexamples of criteria for narrowing the set of predictive models havebeen described above. Optionally, block 1704 and/or block 1706 may berepeated with the reduced set of predictive models (e.g., predictivemodels 1412 followed by predictive models 1414) unless an endingcondition is satisfied. The ending condition could be based on a varietyof possible factors including a number of iterations or repetitionsperformed, an amount of error in one or more of the models, the modeltype of one or more remaining models, and more.

Based on the evaluation of the predictive models, at least one of theremaining models in the set (e.g., one or more predictive models) isused for anomaly detection. This set may include selected model 1416. Asshown, removing a predictive model from the set may in some cases beoptional at any given iteration. For example, in some iterations, nomodel may be removed from the set. Further, a model may be selected foranomaly detection at any time in method 1700 and in any iteration. It isalso noted the various evaluation and removal criteria can change indifferent instances of blocks 1706 and 1708.

At block 1710, transmission is caused of an indication of an anomalydetected by the anomaly detection. For example, anomaly detection tool1316 can cause transmission of an indication of an anomaly detectedusing the selected predictive model. In some cases, the indication maycorrespond to indications 1502, 1512, 1514, or 1516 of FIG. 15.

Turning now to FIG. 18, FIG. 18 illustrates method 1800 in accordancewith embodiments of the present application. Such a method may beperformed, for example, at an anomaly detection tool, such as anomalydetection tool 1316 of FIG. 13. At block 1802, a set of time series areidentified for anomaly detection. For example, anomaly detection tool1316 may identify the set of time series based on a user selectionassociated with the time series and/or a user identification or otheruser interaction associated with the time series.

At block 1804, the set of time series are clustered. For example, basedon the identifying of the set, anomaly detection tool 1316 may clusterthe set of time series, resulting in a set of clusters each including atleast one of the time series.

At block 1806, a representative time series is determined for a cluster.For example, for each cluster anomaly detection tool 1316 may determinea representative time series. A representative time series for a clustermay correspond to any number of the time series in a cluster. Forexample, anomaly detection tool 1316 may use a time series of thecluster as the representative time series, or use a time seriescorresponding to an aggregation of multiple ones of the time series.

At block 1808, a predictive model is determined for the representativetime series. For example, for each cluster, anomaly detection tool 1316may determine a predictive model configured to predict values associatedwith the corresponding representative time series using valuesassociated with at least one time series of another cluster (e.g.,corresponding to a representative time series of another cluster and/ora time series of the cluster). By way of example, at least one of thepredictive models may be determined in accordance with the descriptionof FIGS. 14A and 14B.

At block 1810, anomaly detection is performed using the predictivemodel. For example, anomaly detection tool 1316 may perform anomalydetection on a time series associated with the cluster. This could beperformed on values associated with the representative time series,and/or the multiple time series of the set of time series. Anomalydetection tool 1316 may in some cases perform anomaly detection whichcovers each time series in the set of time series. Any number ofpredictive models or combinations thereof may be employed. Anomalydetection tool 1316 may cause transmission of an indicator based ondetecting one or more anomalies associated with the set of time series.It is noted indicators as used herein, need not correspond toinformation presented or displayed to a user. For example, an indicatormay trigger some action by one or more computing devices, such as acorrective, remedial, or security-related action or function.

3.5 Illustrative Hardware System

The systems and methods described above may be implemented in a numberof ways. One such implementation includes computer devices havingvarious electronic components. For example, components of the system inFIG. 13 may, individually or collectively, be implemented with deviceshaving one or more Application Specific Integrated Circuits (ASICs)adapted to perform some or all of the applicable functions in hardware.Alternatively, the functions may be performed by one or more otherprocessing units (or cores), on one or more integrated circuits orprocessors in programmed computers. In other embodiments, other types ofintegrated circuits may be used (e.g., Structured/Platform ASICs, FieldProgrammable Gate Arrays (FPGAs), and other Semi-Custom ICs), which maybe programmed in any manner known in the art. The functions of each unitmay also be implemented, in whole or in part, with instructions embodiedin a memory, formatted to be executed by one or more general orapplication-specific computer processors.

An example operating environment in which embodiments of the presentdisclosure may be implemented is described below in order to provide ageneral context for various aspects of the present disclosure. Referringto FIG. 19, an illustrative operating environment for implementingembodiments of the present disclosure is shown and designated generallyas computing device 1900. Computing device 1900 is but one example of asuitable operating environment and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.Neither should the computing device 1900 be interpreted as having anydependency or requirement relating to any one or combination ofcomponents illustrated.

The invention may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Theinvention may be practiced in a variety of system configurations,including handheld devices, consumer electronics, general-purposecomputers, more specialized computing devices, etc. The invention mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

With reference to FIG. 19, computing device 1900 includes a bus 1910that directly or indirectly couples the following devices: memory 1912,one or more processors 1914, one or more presentation components 1916,input/output (I/O) ports 1918, I/O components 1920, and an illustrativepower supply 1922. Bus 1910 represents what may be one or more busses(such as, for example, an address bus, data bus, or combinationthereof). Although depicted in FIG. 19, for the sake of clarity, asdelineated boxes that depict groups of devices without overlap betweenthese groups of devices, in reality, this delineation is not so clearcut and a device may well fall within multiple ones of these depictedboxes. For example, one may consider a display to be one of the one ormore presentation components 1916 while also being one of the I/Ocomponents 1920. As another example, processors have memory integratedtherewith in the form of cache; however, there is no overlap depictedbetween the one or more processors 1914 and the memory 1912. A person ofskill in the art will readily recognize that such is the nature of theart, and it is reiterated that the diagram of FIG. 19 merely depicts anillustrative computing device that can be used in connection with one ormore embodiments of the present disclosure. It should also be noticedthat distinction is not made between such categories as “workstation,”“server,” “laptop,” “handheld device,” etc., as all such devices arecontemplated to be within the scope of computing device 1900 of FIG. 19and any other reference to “computing device,” unless the contextclearly indicates otherwise.

Computing device 1900 typically includes a variety of computer-readablemedia. Computer-readable media can be any available media that can beaccessed by computing device 1900 and includes both volatile andnonvolatile media, and removable and non-removable media. By way ofexample, and not limitation, computer-readable media may comprisecomputer storage media and communication media. Computer storage mediaincludes both volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage of informationsuch as computer-readable instructions, data structures, programmodules, or other data. Computer storage media includes, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired information and which can be accessed by computing device2400. Computer storage media does not comprise signals per se, such as,for example, a carrier wave. Communication media typically embodiescomputer-readable instructions, data structures, program modules, orother data in a modulated data signal such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia includes wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared, and otherwireless media. Combinations of any of the above should also be includedwithin the scope of computer-readable media.

Memory 1912 includes computer storage media in the form of volatileand/or nonvolatile memory. The memory may be removable, non-removable,or a combination thereof. Typical hardware devices may include, forexample, solid-state memory, hard drives, optical-disc drives, etc.Computing device 1900 includes one or more processors 1914 that readdata from various entities such as memory 1912 or I/O components 1920.Presentation component(s) 1916 present data indications to a user orother device. Illustrative presentation components include a displaydevice, speaker, printing component, vibrating component, etc.

I/O ports 1918 allow computing device 1900 to be logically coupled toother devices including I/O components 1920, some of which may be builtin. Illustrative components include a keyboard, mouse, microphone,joystick, game pad, satellite dish, scanner, printer, wireless device,etc. The I/O components 1920 may provide a natural user interface (NUI)that processes air gestures, voice, or other physiological inputsgenerated by a user. In some instances, inputs may be transmitted to anappropriate network element for further processing. An NUI may implementany combination of speech recognition, stylus recognition, facialrecognition, biometric recognition, gesture recognition both on screenand adjacent to the screen, air gestures, head and eye tracking, andtouch recognition (as described elsewhere herein) associated with adisplay of the computing device 1900. The computing device 1900 may beequipped with depth cameras, such as stereoscopic camera systems,infrared camera systems, RGB camera systems, touchscreen technology, andcombinations of these, for gesture detection and recognition.Additionally, the computing device 1900 may be equipped withaccelerometers or gyroscopes that enable detection of motion.

As can be understood, implementations of the present disclosure providefor various approaches to data processing. The present invention hasbeen described in relation to particular embodiments, which are intendedin all respects to be illustrative rather than restrictive. Alternativeembodiments will become apparent to those of ordinary skill in the artto which the present invention pertains without departing from itsscope.

From the foregoing, it will be seen that this invention is one welladapted to attain all the ends and objects set forth above, togetherwith other advantages which are obvious and inherent to the system andmethod. It will be understood that certain features and subcombinationsare of utility and may be employed without reference to other featuresand subcombinations. This is contemplated by and is within the scope ofthe claims.

What is claimed is:
 1. A computer-implemented method comprising:obtaining sequences of time series values determined from raw machinedata, each sequence corresponding to a respective time series, whereinthe raw machine data is produced by one or more components within aninformation technology or security environment and reflects activitywithin the information technology or security environment; identifying apredictive model for a first time series based on the sequences of timeseries values, the predictive model trained over a training period togenerate predicted values associated with the first time series usingtime series values corresponding to a second time series; evaluating oneor more characteristics of the predictive model; and selecting thepredictive model for anomaly detection based on the evaluating of theone or more characteristics.
 2. The method of claim 1, furthercomprising applying the selected predictive model to subsequentlyreceived time series values of the second time series to detect ananomaly.
 3. The method of claim 1, wherein the evaluating comprisesdetermining an error between the predicted values and values associatedwith the first time series, wherein the selecting of the predictivemodel is based on the error of the predictive model.
 4. The method ofclaim 1, wherein the one or more characteristics correspond to residualsbetween the predicted values of the predictive model and time seriesvalues associated with the first time series.
 5. The method of claim 1comprising: further training the predictive model over a subsequenttraining period based on the evaluating of the one or morecharacteristics; and further evaluating the predictive model based onthe further training, wherein the predictive model is selected for theanomaly detection based on the further evaluating.
 6. The method ofclaim 1 comprising: training the predictive model using first portionsof the sequences of time series values corresponding to the trainingperiod; and analyzing second portions of the sequences of time seriesvalues corresponding to a prediction period, wherein the one or morecharacteristics are based on the analyzed second portions.
 7. The methodof claim 1, further comprising clustering the sequences of time seriesvalues into a plurality of clusters, wherein the first time seriescorresponds to a representative time series of a first of the pluralityof clusters and the second time series corresponds to at least one timeseries in a second cluster of the plurality of clusters.
 8. The methodof claim 1, wherein the obtaining of the sequences of time series valuesis responsive to a user interaction associated with the sequences of theseries values.
 9. The method of claim 1, wherein the sequences of timeseries values correspond to at least one of performance metrics orsecurity-related metrics.
 10. The method of claim 1, wherein thepredictive model comprises one or more of a polynomial model, a neuralnetwork, or a decision tree model.
 11. The method of claim 1, whereinthe obtaining of the sequences of time series values is from one or morestreams of time series data.
 12. The method of claim 1, wherein the oneor more characteristics are based on a first explanatory valueassociated with a model type of the predictive model.
 13. The method ofclaim 1, wherein the values of the second time series correspond toevents, each event comprising a time stamp and a portion of raw data.14. The method of claim 1, wherein each data point of the second timeseries is associated with a respective time stamp of a respective event.15. The method of claim 1, comprising generating the time series valuesfrom event data using a late-binding schema.
 16. The method of claim 1,wherein the predicted values are associated with later times than thevalues of the second time series used to generate the predictive model.17. The method of claim 1, further comprising causing an explanatorymessage to be presented based on the anomaly detection, the explanatorymessage indicating a predicted relationship corresponding to thepredictive model and an observed relationship associated with ananomaly.
 18. The method of claim 1, wherein the selecting is of multiplemodels including the predictive model based on the evaluating of the oneor more characteristics, and the anomaly is detected using the multiplemodels.
 19. One or more non-transitory computer-readable storage mediahaving instructions stored thereon, wherein the instructions, whenexecuted by one or more processors, cause the one or more processors toperform a computer-implemented method comprising: obtaining sequences oftime series values determined from raw machine data, each sequencecorresponding to a respective time series, wherein the raw machine datais produced by one or more components within an information technologyor security environment and reflects activity within the informationtechnology or security environment; identifying a predictive model for afirst time series based on the sequences of time series values, thepredictive model trained over a training period to generate predictedvalues associated with the first time series using time series valuescorresponding to a second time series; evaluating one or morecharacteristics of the predictive model; and selecting the predictivemodel for anomaly detection based on the evaluating of the one or morecharacteristics.
 20. The one or more computer-readable storage media ofclaim 19, wherein the method further comprises applying the selectedpredictive model to subsequently received time series values of thesecond time series to detect an anomaly.
 21. The one or morecomputer-readable storage media of claim 19, wherein the evaluatingcomprises determining an error between the predicted values and valuesassociated with the first time series, wherein the selecting of thepredictive model is based on the error of the predictive model.
 22. Theone or more computer-readable storage media of claim 19, wherein the oneor more characteristics correspond to residuals between the predictedvalues of the predictive model and time series values associated withthe first time series.
 23. The one or more computer-readable storagemedia of claim 19, wherein the method further comprises: furthertraining the predictive model over a subsequent training period based onthe evaluating of the one or more characteristics; and furtherevaluating the predictive model based on the further training, whereinthe predictive model is selected for the anomaly detection based on thefurther evaluating.
 24. The one or more computer-readable storage mediaof claim 19, wherein the method further comprises: training thepredictive model using first portions of the sequences of time seriesvalues corresponding to the training period; and analyzing secondportions of the sequences of time series values corresponding to aprediction period, wherein the one or more characteristics are based onthe analyzed second portions.
 25. A computer-implemented systemcomprising: one or more hardware processors; one or morecomputer-readable storage media having instructions stored thereon,wherein the instructions, when executed by the one or more processors,cause the one or more processors to perform a method comprising:obtaining sequences of time series values determined from raw machinedata, each sequence corresponding to a respective time series, whereinthe raw machine data is produced by one or more components within aninformation technology or security environment and reflects activitywithin the information technology or security environment; identifying apredictive model for a first time series based on the sequences of timeseries values, the predictive model trained over a training period togenerate predicted values associated with the first time series usingtime series values corresponding to a second time series; evaluating oneor more characteristics of the predictive model; and selecting thepredictive model for anomaly detection based on the evaluating of theone or more characteristics.
 26. The system of claim 25, wherein themethod further comprises applying the selected predictive model tosubsequently received time series values of the second time series todetect an anomaly.
 27. The system of claim 25, wherein the evaluatingcomprises determining an error between the predicted values and valuesassociated with the first time series, wherein the selecting of thepredictive model is based on the error of the predictive model.
 28. Thesystem of claim 25, wherein the one or more characteristics correspondto residuals between the predicted values of the predictive model andtime series values associated with the first time series.
 29. The systemof claim 25, wherein the method further comprises: further training thepredictive model over a subsequent training period based on theevaluating of the one or more characteristics; and further evaluatingthe predictive model based on the further training, wherein thepredictive model is selected for the anomaly detection based on thefurther evaluating.
 30. The system of claim 25, wherein the methodfurther comprises: training the predictive model using first portions ofthe sequences of time series values corresponding to the trainingperiod; and analyzing second portions of the sequences of time seriesvalues corresponding to a prediction period, wherein the one or morecharacteristics are based on the analyzed second portions.